Is Motor‐Imagery Brain‐Computer Interface Feasible in Stroke Rehabilitation?. Issue 8 (12th January 2014)

• Record Type: Journal Article
• Authors: Teo, Wei‐Peng; Chew, Effie
• Is Part Of: PM&R. Volume 6:Issue 8(2014)
• Page Start: 723
• Page End: 728
• Publication Date: 2014-01-12
• Subjects: Medical rehabilitation -- Periodicals; Physical therapy -- Periodicals; Physical Therapy Modalities -- Periodicals; 615.5
• DOI: 10.1016/j.pmrj.2014.01.006
• Languages: English
• ISSNs: 1934-1482

Abstract

Abstract : In the past 3 decades, interest has increased in brain‐computer interface (BCI) technology as a tool for assisting, augmenting, and rehabilitating sensorimotor functions in clinical populations. Initially designed as an assistive device for partial or total body impairments, BCI systems have since been explored as a possible adjuvant therapy in the rehabilitation of patients who have had a stroke. In particular, BCI systems incorporating a robotic manipulanda to passively manipulate affected limbs have been studied. These systems can use a range of invasive (ie, intracranial implanted electrodes) or noninvasive neurophysiologic recording techniques (ie, electroencephalography [EEG], near‐infrared spectroscopy, and magnetoencephalography) to establish communication links between the brain and the BCI system. Trials are most commonly performed on EEG‐based BCI in comparison with the other techniques because of its high temporal resolution, relatively low setup costs, portability, and noninvasive nature. EEG‐based BCI detects event‐related desynchronization/synchronization in sensorimotor oscillatory rhythms associated with motor imagery (MI), which in turn drives the BCI. Previous evidence suggests that the process of MI preferentially activates sensorimotor regions similar to actual task performance and that repeated practice of MI can induce plasticity changes in the brain. It is therefore postulated that the combination of MI and BCI may augment rehabilitation gains in patients who have had a stroke by activating corticomotor networks via MI and providing sensory feedback from the affected limb using end‐effector robots. In this review we examine the current literature surrounding the feasibility of EEG‐based MI‐BCI systems in stroke rehabilitation. We also discuss the limitations of using EEG‐based MI‐BCI in patients who have had a stroke and suggest possible solutions to overcome these limitations.